Generative AI Engineer

Iris client, one of their Auditing Client is looking to hire AI Engineer for Remote. Job Title: AI EngineerLocation: RemoteMajor skills: .NET Fullstack, C#, AZURE, AI, Azure AI Skillset: • Languages and frameworks: .NET / C#, Python, JavaScript / TypeScript, PowerShell, Angular • Cloud and infrastructure: Microsoft Azure, Terraform, Infrastructure as Code (IaC), cloud-native architecture patterns • DevOps and delivery: GitHub, GitHub Actions, CI/CD pipeline design and implementation • Cross-cutting: API design, observability, security practices, dependency management • AI-Augmented Engineering • You use AI tooling actively across your development workflow — code generation, review, documentation, debugging, exploration, test scaffolding — and you have a clear, opinionated view of where it adds value and where it does not. • You understand enough about how large language models work — context windows, prompting patterns, retrieval-augmented generation, agent architectures — to be a capable practitioner without needing to be a researcher. • You are proficient with current AI development tooling: IDE-integrated assistants, agentic workflows, prompt engineering, and the emerging ecosystem of AI-native developer tools. • You can communicate your AI workflow to others clearly — what you use, when you use it, what you still do yourself, and what safety or quality checks you apply to AI-assisted output. • You recognize that AI amplifies strong engineers and cannot substitute for weak foundations. You bring both. Responsibilities: • Design and deliver software solutions across the full engineering lifecycle — from architecture and infrastructure through to application logic, integration, testing, and deployment. • Contribute to platform and tooling decisions, not just implementation. • Bring engineering rigour to every layer of the stack: infrastructure, backend services, APIs, front-end experiences, pipelines, and automation. • Write code that is production-ready from the start — tested, documented, observable, and maintainable. • Make deliberate use of AI tooling throughout your development workflow, and help raise the standard for how the wider team works with AI. • Mentor and influence peers through code review, pair programming, technical documentation, and architectural input. • Move fluidly between domains as delivery demands it, while going deep where depth is needed.